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Spiking neural networks (SNNs) are brain-inspired machine learning algorithmswith merits such as biological plausibility and unsupervised learningcapability. Previous works have shown that converting Artificial NeuralNetworks (ANNs) into SNNs is a practical and efficient approach forimplementing an SNN. However, the basic principle and theoretical groundworkare lacking for training a non-accuracy-loss SNN. This paper establishes aprecise mathematical mapping between the biological parameters of the LinearLeaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/DeepNeural Networks (DNNs). Such mapping relationship is analytically proven undercertain conditions and demonstrated by simulation and real data experiments. Itcan serve as the theoretical basis for the potential combination of therespective merits of the two categories of neural networks.

Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks